class_docstr: A wrapper that facilitates the use of `TabularSampler` for distributed sampling.
docstr: Wrapper around :py:meth:`alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__`. It allows sampling a
  batch of anchors in the same process, which can improve performance.
  
  Parameters
  ----------
  anchors_batch, num_samples, compute_labels
      A list of result tuples. See :py:meth:`alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__`
      for details.
docstr_tok: ["Wrapper","around",":","py",":","meth",":","`","alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__","`",".","It","allows","sampling","a","batch","of","anchors","in","the","same","process",",","which","can","improve","performance",".","Parameters","--","--","--","--","--","anchors_batch",",","num_samples",",","compute_labels","A","list","of","result","tuples",".","See",":","py",":","meth",":","`","alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__","`","for","details","."]
code: def __call__(
      self,
      anchors_batch: Union[Tuple[int, tuple], List[Tuple[int, tuple]]],
      num_samples: int,
      compute_labels: bool = True,
  ) -> List:
      """
      Wrapper around :py:meth:`alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__`. It allows sampling a
      batch of anchors in the same process, which can improve performance.
  
      Parameters
      ----------
      anchors_batch, num_samples, compute_labels
          A list of result tuples. See :py:meth:`alibi.explainers.anchors.anchor_tabular.TabularSampler.__call__`
          for details.
      """
  
      if isinstance(
          anchors_batch, tuple
      ):  # DistributedAnchorBaseBeam._get_samples_coverage call
          return self.sampler(
              anchors_batch, num_samples, compute_labels=compute_labels
          )
      elif len(anchors_batch) == 1:  # batch size = 1
          return [
              self.sampler(
                  *anchors_batch,
                  num_samples,
                  compute_labels=compute_labels,
              )
          ]
      else:  # batch size > 1
          batch_result = []
          for anchor in anchors_batch:
              batch_result.append(
                  self.sampler(
                      anchor, num_samples, compute_labels=compute_labels
                  )
              )
  
          return batch_result
  
imports: import logging
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import ray
  from alibi.api.interfaces import Explanation
  from alibi.utils.discretizer import Discretizer
  from alibi.utils.mapping import ohe_to_ord
  from .anchor_base import AnchorBaseBeam
  from .anchor_tabular import AnchorTabular, TabularSampler
  from alibi.utils.distributed import ActorPool
  from functools import partial
label_desc: Use image node_id 2 for calling the RemoteSampler obj's underlying member method code with example usage: obj.__call__(anchors_batch, num_samples, compute_labels) and returns: self, list, batch_result